Student Perceptions Change in a Chemical Engineering Class using Cooperative Problem Based Learning (CPBL)

AbstractThis paper reports a phenomenological study of a class of third year Chemical Engineering students first experience in undergoing a course using Cooperative Problem Based Learning (CPBL). The main purpose of this study is to understand the students’ perceptions on CPBL in two aspects; (1) the student perceptions and acceptance on the learning approach; and (2) what the students gained from the learning process. The paper illustrates the pattern of perception change among the students and how CPBL affects the students’ mastery of the content knowledge (Process Control), problem-solving, team-working as well as self-esteem. Concurrently, this study also investigates the role played by the lecturer in affecting the students’ perception change. Through classroom observations and interviews for one whole semester, the results are reported in three stages: (1) the beginning; (2) the middle; and (3) the end of the semester. The findings have wider relevance for evaluating student assessments of CPBL in Engineering Education

Optimal set of EEG features for emotional state classification and trajectory visualization in Parkinson's disease

In addition to classic motor signs and symptoms, individuals with Parkinson's disease (PD) are characterized by emotional deficits. Ongoing brain activity can be recorded by electroencephalograph (EEG) to discover the links between emotional states and brain activity. This study utilized machine-learning algorithms to categorize emotional states in PD patients compared with healthy controls (HC) using EEG. Twenty non-demented PD patients and 20 healthy age-, gender-, and education level-matched controls viewed happiness, sadness, fear, anger, surprise, and disgust emotional stimuli while fourteen-channel EEG was being recorded. Multimodal stimulus (combination of audio and visual) was used to evoke the emotions. To classify the EEG-based emotional states and visualize the changes of emotional states over time, this paper compares four kinds of EEG features for emotional state classification and proposes an approach to track the trajectory of emotion changes with manifold learning. From the experimental results using our EEG data set, we found that (a) bispectrum feature is superior to other three kinds of features, namely power spectrum, wavelet packet and nonlinear dynamical analysis; (b) higher frequency bands (alpha, beta and gamma) play a more important role in emotion activities than lower frequency bands (delta and theta) in both groups and; (c) the trajectory of emotion changes can be visualized by reducing subject-independent features with manifold learning. This provides a promising way of implementing visualization of patient's emotional state in real time and leads to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing

Objective: While Parkinson’s disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study was to investigate whether there are specific electroencephalographic (EEG) characteristics that discriminate PD patients and normal controls during emotion information processing. Method: EEG recordings from 14 scalp sites were collected from 20 PD patients and 30 age-matched normal controls. Multimodal (audio-visual) stimuli were presented to evoke specific targeted emotional states such as happiness, sadness, fear, anger, surprise and disgust. Absolute and relative power, frequency and asymmetry measures derived from spectrally analyzed EEGs were subjected to repeated ANOVA measures for group comparisons as well as to discriminate function analysis to examine their utility as classification indices. In addition, subjective ratings were obtained for the used emotional stimuli. Results: Behaviorally, PD patients showed no impairments in emotion recognition as measured by subjective ratings. Compared with normal controls, PD patients evidenced smaller overall relative delta, theta, alpha and beta power, and at bilateral anterior regions smaller absolute theta, alpha, and beta power and higher mean total spectrum frequency across different emotional states. Inter-hemispheric theta, alpha, and beta power asymmetry index differences were noted, with controls exhibiting greater right than left hemisphere activation. Whereas intra-hemispheric alpha power asymmetry reduction was exhibited in patients bilaterally at all regions. Discriminant analysis correctly classified 95.0% of the patients and controls during emotional stimuli. Conclusion: These distributed spectral powers in different frequency bands might provide meaningful information about emotional processing in PD patients

Cognitive impairment and memory dysfunction after a stroke diagnosis: a post-stroke memory assessment

Cognitive impairment and memory dysfunction following stroke diagnosis are common symptoms that significantly affect the survivors’ quality of life. Stroke patients have a high potential to develop dementia within the first year of stroke onset. Currently, efforts are being exerted to assess stroke effects on the brain, particularly in the early stages. Numerous neuropsychological assessments are being used to evaluate and differentiate cognitive impairment and dementia following stroke. This article focuses on the role of available neuropsychological assessments in detection of dementia and memory loss after stroke. This review starts with stroke types and risk factors associated with dementia development, followed by a brief description of stroke diagnosis criteria and the effects of stroke on the brain that lead to cognitive impairment and end with memory loss. This review aims to combine available neuropsychological assessments to develop a post-stroke memory assessment (PSMA) scheme based on the most recognized and available studies. The proposed PSMA is expected to assess different types of memory functionalities that are related to different parts of the brain according to stroke location. An optimal therapeutic program that would help stroke patients enjoy additional years with higher quality of life is presented

Effect of substituting concentrate with dwarf Napier grass (Pennisetum purpureum) on intake, growth and carcass composition of rabbits

Dwarf Napier grass (Pennisetum purpureum) is considered to be more suitable as forage for ruminants due to its high yield and nutritive value, but there is limited research on rabbits. Thus, the aim of this study was to investigate the effect of replacing concentrate with dwarf Napier grass on intake, gorwth performance and carcass composition of growing rabbits. Twelve growing rabbits were randomly distributed into three dietary groups in a completely randomised design: (i) concentrate feed ad libitum as control diet (T1), (ii) half of the control diet plus dwarf Napier grass ad libitum (T2), and (iii) quarter of the control diet plus dwarf Napier grass ad libitum (T3). The results showed that diets had a significant (p<0.05) effect on intake, growth performance and some non-carcass components. Rabbits fed T3 diet showed significantly (p<0.05) lower total DM intake than those fed T1 and T2 diets. Similarly, rabbits fed T3 diet showed significantly (p<0.05) lower total weight gain and daily weight gain than those fed T1 diet, but the respective values of those fed T2 diet were non-signifiacntly different from those fed T1 and T3 diets. There was significant effect on weights of meat with bone, fat, pelt, head and kidney by the diets, whereas weights of most of the non-carcass components were similar among the groups. In conclusion, diet consisting of half of the concentrate and dwarf Napier grass ad libitum is recommended to be used as it may reduce the feed cost compared to diet consisting of the concentrate alone

Entomofaunal diversity of Hymenoptera at Hutan Simpan UiTM Jengka, Kem Sri Gading

A study on abundance and diversity of Hymenoptera was conducted in Hutan Simpan UiTM Jengka Kem Sri Gading to determine the relationship of diversity of Hymenoptera with the environmental gradient. The samplings were conducted from November 2013 to April 2014 using Malaise traps. Three malaise traps were installed at the forest fringe and another tree were placed at the inner forest. A total of 286 Hymenopteran comprising of 15 subfamilies and 63 species (morphospecies) were collected. The families identified were Icheneumonidae, Sphecidae, Braconidae, Formicidae, Vespidae, Pompilidae, Apidae, Tiphiidae, Bethylidae, Ampolicidae, Thynnidae, Evaniidae, Gasteruptiidae, Pelecinidae and Rhopalosomatidae. Overall result showed that Ichneumonidae was the most abundant family with 56 individuals while family Gasteruptiidae was the least abundant family with only one individual recorded. Inner forest had the most individual collected with 191 individuals that comprise of 12 families and 48 morphospecies. On the other hand, forest fringe recorded only 95 individuals (11 families and 28 morphospecies). Shannon Weiner Diversity Index (H’) showed that each studied plots did not differs significantly (P>0.05) with inner forest having the highest diversity value for Hymenoptera with H’=2.13 while forest fringe recorded H’=2.09. Evenness index for both study sites recorded the same value of E’=0.88. For the Margalef index, inner forest recorded R’=2.09 slight lower than forest fringe with R’=2.19. As a conclusion, this study suggests that diversity and abundance of Hymenoptera was higher at inner forest compared to forest fringe. Overall study showed that the diversity and abundance of Hymenoptera in both study sites were low since the value of H’ were less than 3.50

Development of students’ knowledge-behavioural changes in relation to sustainability through a case study

The purpose of this study is to develop students’ knowledge and behavioural changes in relation to sustainability through a case study. Cooperative Problem-Based Learning (CPBL) was used as a teaching and learning approach among the first year chemical engineering students. A case study was designed to create a learning environment where students involved in the ‘Waste to Wealth’ Campus Contest. A quantitative method was conducted. Data for the research were gathered through administrated a survey instrument at the beginning and end of semester. Structure of Observed Learning Outcomes (SOLO) taxonomy and Precaution Adoption Process Model (PAPM) of changing individual behaviour were used to measure the levels of students’ knowledge and behavioural changes, respectively. The findings showed that CPBL significantly developed students’ knowledge, and behavioural changes towards instilling the awareness of sustainable development

Emotion classification in Parkinson's disease by higher-order spectra and power spectrum features using EEG signals: A comparative study

Deficits in the ability to process emotions characterize several neuropsychiatric disorders and are traits of Parkinson's disease (PD), and there is need for a method of quantifying emotion, which is currently performed by clinical diagnosis. Electroencephalogram (EEG) signals, being an activity of central nervous system (CNS), can reflect the underlying true emotional state of a person. This study applied machine-learning algorithms to categorize EEG emotional states in PD patients that would classify six basic emotions (happiness and sadness, fear, anger, surprise and disgust) in comparison with healthy controls (HC). Emotional EEG data were recorded from 20 PD patients and 20 healthy age-, education level- and sex-matched controls using multimodal (audio-visual) stimuli. The use of nonlinear features motivated by the higher-order spectra (HOS) has been reported to be a promising approach to classify the emotional states. In this work, we made the comparative study of the performance of k-nearest neighbor (kNN) and support vector machine (SVM) classifiers using the features derived from HOS and from the power spectrum. Analysis of variance (ANOVA) showed that power spectrum and HOS based features were statistically significant among the six emotional states (p < 0.0001). Classification results shows that using the selected HOS based features instead of power spectrum based features provided comparatively better accuracy for all the six classes with an overall accuracy of 70.10% ± 2.83% and 77.29% ± 1.73% for PD patients and HC in beta (13-30 Hz) band using SVM classifier. Besides, PD patients achieved less accuracy in the processing of negative emotions (sadness, fear, anger and disgust) than in processing of positive emotions (happiness, surprise) compared with HC. These results demonstrate the effectiveness of applying machine learning techniques to the classification of emotional states in PD patients in a user independent manner using EEG signals. The accuracy of the system can be improved by investigating the other HOS based features. This study might lead to a practical system for noninvasive assessment of the emotional impairments associated with neurological disorders

The roles of soybean lecithin in aquafeed: a crucial need and update

Soybean lecithin is extensively used as the dietary supplementation of phospholipids in animal production. Soybean lecithin plays significant roles in aquafeed as growth promoter, feed enhancer, immunity modulator and antioxidant activity stimulator for aquaculture species. Besides, soybean lecithin is also reported to help aquaculture species being resilient to physical and chemical stressors. In this review, common sources, chemical structure and mode of action of lecithin, with highlight on soybean lecithin application in aquaculture over four-decadal studies published between 1983 and 2023, were evaluated and summarized. By far, soybean lecithin is best-known for its beneficial effects, availability yet cost-effective for aquafeed formulation. Findings from this review also demonstrate that although nutritional profile of long-chain polyunsaturated fatty acids and phosphatidylcholine from egg yolk and marine sources are superior to those from plant sources such as soybean, it is rather costly for sustainable application in aquafeed formulation. Moreover, commercially available products that incorporate soybean lecithin with other feed additives are promising to boost aquaculture production. Overall, effects of soybean lecithin supplementation are well-recognized on larval and juvenile of aquaculture species which having limited ability to biosynthesis phospholipids de novo, and correspondingly attribute to phospholipid, a primary component of soybean lecithin, that is essential for rapid growth during early stages development. In addition, soybean lecithin supplementation plays a distinguish role in stimulating maturation of gonadal development in the adults, especially for crustaceans

Detection of emotions in Parkinson's disease using higher order spectral features from brain's electrical activity

Non-motor symptoms in Parkinson's disease (PD) involving cognition and emotion have been progressively receiving more attention in recent times. Electroencephalogram (EEG) signals, being an activity of central nervous system, can reflect the underlying true emotional state of a person. This paper presents a computational framework for classifying PD patients compared to healthy controls (HC) using emotional information from the brain's electrical activity